<![CDATA[General Background: The online transportation industry is becoming increasingly competitive, necessitating a deeper understanding of factors that affect workforce sustainability. Specific Background: Within algorithm-driven gig platforms, driver-partners operate under flexible yet demanding conditions, influencing their performance and well-being. Knowledge Gap: Despite the growing literature on digital labor, limited research has examined how individual factors like competence, workload, and incentives impact job satisfaction and performance in such decentralized systems. Aims: This study investigates the effects of competence, workload, and incentives on job satisfaction and how these in turn influence the performance of driver-partners in an online food delivery platform. Results: Using Structural Equation Modeling (SEM) with AMOS and data from 100 respondents, findings reveal that only workload significantly affects job satisfaction. Competence and incentives showed no significant relationship, nor did job satisfaction or the examined factors significantly impact performance. Novelty: The study challenges traditional assumptions by showing that in platform-based work environments, personal variables may not directly drive performance outcomes.Implications: These results suggest a shift in managerial focus—from individual optimization to systemic interventions—better suited for digital labor ecosystems governed by algorithms and flexible task structures. Highlights: Only workload significantly influences job satisfaction. Competence and incentives do not directly impact satisfaction or performance. System-level strategies are more effective than personal improvements. Keywords: Job Satisfaction, Driver Performance, Online Food Delivery, Workload Impact, Digital Labor]]>
